This series of files compile all analyses done during Chapter 3:
- Section 1 presents the calculation of the indices of exposure.
- Section 2 presents variable exploration and regressions results.
- Section 3 presents species distribution models.
All analyses have been done with R 4.1.0.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
⏪ | 🏠 | ⏩
1. Spatial variation of exposure indices
Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).
Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

2. Relationships with abiotic parameters and biotic indices
Biotic indices have been calculated during Chapter 2.
2.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models are implemented now, as there are some bugs with the automatized calculation of the others.
Aquaculture


Dredging


Runoff


Sewers


Structures


Shipping


Fisheries


Cumulative exposure


2.2. Correlation
Correlations have been calculated with Spearman’s rank coefficient.
Correlation coefficients between exposure and variables/indices
| aquaculture |
-0.088 |
0.065 |
0.023 |
-0.025 |
0.051 |
-0.282 |
-0.24 |
-0.355 |
-0.476 |
-0.483 |
-0.479 |
-0.286 |
-0.267 |
-0.413 |
-0.02 |
-0.055 |
0.158 |
0.152 |
0.209 |
0.223 |
0.191 |
0.157 |
0.262 |
0.204 |
0.18 |
-0.14 |
0.036 |
0.068 |
-0.01 |
-0.192 |
| dredging |
0.238 |
-0.02 |
-0.012 |
0.003 |
0.044 |
0.062 |
0.004 |
0.257 |
0.373 |
0.566 |
0.369 |
-0.025 |
0.126 |
0.28 |
-0.167 |
0.107 |
0.059 |
-0.164 |
-0.05 |
-0.132 |
-0.019 |
0.052 |
-0.027 |
-0.087 |
0.074 |
0.081 |
0.06 |
-0.115 |
-0.015 |
0.155 |
| runoff |
-0.016 |
-0.075 |
0.297 |
-0.194 |
0.018 |
-0.149 |
-0.093 |
0.048 |
0.299 |
0.278 |
0.154 |
-0.126 |
-0.027 |
0.147 |
-0.059 |
-0.068 |
-0.028 |
-0.162 |
-0.065 |
-0.181 |
-0.012 |
0.03 |
-0.085 |
-0.158 |
-0.034 |
0.112 |
0.01 |
0.011 |
-0.004 |
0.031 |
| sewers |
0.424 |
-0.14 |
-0.387 |
0.344 |
0.164 |
0.639 |
0.588 |
0.691 |
0.751 |
0.669 |
0.755 |
0.642 |
0.706 |
0.743 |
-0.091 |
0.053 |
-0.171 |
-0.248 |
-0.23 |
-0.287 |
-0.194 |
-0.108 |
-0.273 |
-0.238 |
-0.187 |
0.176 |
-0.036 |
-0.177 |
0.205 |
0.253 |
| structures |
0.172 |
-0.071 |
0.045 |
-0.006 |
0.076 |
0.052 |
0.044 |
0.277 |
0.465 |
0.514 |
0.404 |
0.057 |
0.165 |
0.326 |
-0.141 |
0.003 |
-0.031 |
-0.219 |
-0.109 |
-0.225 |
-0.054 |
0.012 |
-0.122 |
-0.174 |
-0.035 |
0.047 |
0.039 |
-0.089 |
0.042 |
0.154 |
| shipping |
0.371 |
-0.223 |
-0.228 |
0.238 |
-0.08 |
0.415 |
0.27 |
0.464 |
0.573 |
0.57 |
0.55 |
0.406 |
0.415 |
0.542 |
-0.071 |
0.054 |
-0.003 |
-0.059 |
-0.035 |
-0.059 |
-0.037 |
-0.036 |
-0.082 |
-0.178 |
-0.095 |
0.315 |
-0.045 |
-0.085 |
0.234 |
0.167 |
| fisheries |
-0.492 |
0.202 |
0.376 |
-0.378 |
-0.138 |
-0.567 |
-0.541 |
-0.552 |
-0.606 |
-0.576 |
-0.585 |
-0.54 |
-0.563 |
-0.613 |
0.173 |
-0.066 |
0.097 |
0.309 |
0.224 |
0.28 |
0.167 |
-0.015 |
0.191 |
0.318 |
0.082 |
-0.267 |
0.143 |
0.203 |
-0.226 |
-0.186 |
| cumulative_exposure |
0.277 |
-0.108 |
-0.058 |
0.086 |
0.087 |
0.19 |
0.144 |
0.357 |
0.556 |
0.58 |
0.471 |
0.196 |
0.298 |
0.445 |
-0.092 |
0.03 |
-0.053 |
-0.164 |
-0.107 |
-0.185 |
-0.088 |
-0.048 |
-0.17 |
-0.145 |
-0.075 |
0.215 |
0.033 |
-0.108 |
0.119 |
0.182 |
p-values of correlation test between exposure indices and variables/indices
| aquaculture |
0.3664 |
0.5051 |
0.8152 |
0.7992 |
0.6021 |
0.003082 |
0.01234 |
0.0001626 |
1.949e-07 |
1.228e-07 |
1.579e-07 |
0.002721 |
0.005256 |
8.895e-06 |
0.8385 |
0.5693 |
0.1014 |
0.1157 |
0.02961 |
0.02045 |
0.04805 |
0.1049 |
0.006131 |
0.03454 |
0.06279 |
0.1478 |
0.7139 |
0.4855 |
0.9216 |
0.04691 |
| dredging |
0.01324 |
0.8387 |
0.9003 |
0.9732 |
0.6495 |
0.5259 |
0.9659 |
0.00727 |
6.998e-05 |
1.771e-10 |
8.557e-05 |
0.799 |
0.1938 |
0.003346 |
0.08431 |
0.269 |
0.5434 |
0.09076 |
0.6071 |
0.1748 |
0.8468 |
0.594 |
0.7845 |
0.3704 |
0.446 |
0.4042 |
0.535 |
0.2368 |
0.8796 |
0.1081 |
| runoff |
0.8701 |
0.4395 |
0.001802 |
0.04443 |
0.8515 |
0.1241 |
0.3393 |
0.623 |
0.001656 |
0.003518 |
0.1125 |
0.1942 |
0.7832 |
0.1285 |
0.5414 |
0.4829 |
0.7721 |
0.09401 |
0.5056 |
0.06025 |
0.9 |
0.7592 |
0.38 |
0.1029 |
0.7294 |
0.2487 |
0.9181 |
0.9107 |
0.9663 |
0.7508 |
| sewers |
4.879e-06 |
0.1485 |
3.54e-05 |
0.0002644 |
0.09067 |
9.873e-14 |
2.137e-11 |
1.233e-16 |
7.406e-21 |
2.565e-15 |
3.667e-21 |
6.987e-14 |
1.478e-17 |
3.514e-20 |
0.3471 |
0.5842 |
0.07716 |
0.00972 |
0.01674 |
0.002579 |
0.04455 |
0.2654 |
0.004234 |
0.01294 |
0.05204 |
0.06905 |
0.7087 |
0.06619 |
0.03313 |
0.008293 |
| structures |
0.07492 |
0.4645 |
0.6446 |
0.9475 |
0.4369 |
0.5895 |
0.6483 |
0.003746 |
3.933e-07 |
1.315e-08 |
1.425e-05 |
0.5586 |
0.08784 |
0.0005685 |
0.1453 |
0.9766 |
0.7536 |
0.02293 |
0.2634 |
0.01934 |
0.576 |
0.8993 |
0.2088 |
0.07181 |
0.7189 |
0.6262 |
0.6861 |
0.3619 |
0.6684 |
0.1117 |
| shipping |
7.89e-05 |
0.02058 |
0.01742 |
0.01331 |
0.4089 |
7.945e-06 |
0.004671 |
4.107e-07 |
9.46e-11 |
1.253e-10 |
7.297e-10 |
1.302e-05 |
7.806e-06 |
1.344e-09 |
0.4653 |
0.5813 |
0.9789 |
0.5475 |
0.7225 |
0.5432 |
0.7071 |
0.7092 |
0.3975 |
0.06563 |
0.3268 |
0.0008844 |
0.6448 |
0.3801 |
0.01464 |
0.08464 |
| fisheries |
6.275e-08 |
0.03585 |
6.17e-05 |
5.585e-05 |
0.1551 |
1.607e-10 |
1.476e-09 |
6.146e-10 |
3.727e-12 |
6.679e-11 |
2.989e-11 |
1.623e-09 |
2.366e-10 |
1.852e-12 |
0.07296 |
0.496 |
0.3185 |
0.001128 |
0.01998 |
0.003357 |
0.0847 |
0.8735 |
0.0474 |
0.0007943 |
0.3984 |
0.005238 |
0.1407 |
0.03473 |
0.01884 |
0.05423 |
| cumulative_exposure |
0.003733 |
0.2652 |
0.5492 |
0.3756 |
0.3707 |
0.04849 |
0.1364 |
0.0001492 |
4.022e-10 |
4.899e-11 |
2.628e-07 |
0.04167 |
0.001703 |
1.356e-06 |
0.3421 |
0.7614 |
0.588 |
0.08962 |
0.2702 |
0.05549 |
0.3636 |
0.6214 |
0.07827 |
0.1342 |
0.4407 |
0.02571 |
0.732 |
0.2679 |
0.2197 |
0.05903 |

3. Relationships with benthic communities
3.1. Species
The most abundant taxa in our study area were:
- Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
- Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)
The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left side) or biomass (right side).


We analyzed if some phyla or species were characteristic of exposure classes, and we calculated the IndVal for each class.
Mean density by class
| Annelida |
27.9 |
34.1 |
24.3 |
NA |
NA |
| Arthropoda |
29.9 |
62.7 |
24.4 |
NA |
NA |
| Cnidaria |
0.025 |
0 |
0 |
NA |
NA |
| Echinodermata |
5.55 |
1.92 |
0.389 |
NA |
NA |
| Mollusca |
14.8 |
13.5 |
11.5 |
NA |
NA |
| Nematoda |
20 |
4.9 |
0 |
NA |
NA |
| Nemertea |
0.3 |
0.08 |
0 |
NA |
NA |
| Sipuncula |
0.175 |
0.4 |
0.111 |
NA |
NA |
Total individuals by class
| Annelida |
1116 |
1703 |
437 |
0 |
0 |
| Arthropoda |
1195 |
3136 |
439 |
0 |
0 |
| Cnidaria |
1 |
0 |
0 |
0 |
0 |
| Echinodermata |
222 |
96 |
7 |
0 |
0 |
| Mollusca |
590 |
677 |
207 |
0 |
0 |
| Nematoda |
799 |
245 |
0 |
0 |
0 |
| Nemertea |
12 |
4 |
0 |
0 |
0 |
| Sipuncula |
7 |
20 |
2 |
0 |
0 |
Mean biomass by class
| Annelida |
0.49 |
0.855 |
4.03 |
NA |
NA |
| Arthropoda |
0.158 |
0.114 |
0.0778 |
NA |
NA |
| Cnidaria |
0.0841 |
0 |
0 |
NA |
NA |
| Echinodermata |
11.4 |
1.25 |
4.5 |
NA |
NA |
| Mollusca |
1.11 |
1.56 |
1.75 |
NA |
NA |
| Nematoda |
0.000867 |
0.000286 |
0 |
NA |
NA |
| Nemertea |
5.5e-05 |
0.0342 |
0 |
NA |
NA |
| Sipuncula |
0.0114 |
0.0111 |
0.00468 |
NA |
NA |
Total biomasses by class
| Annelida |
19.6 |
42.7 |
72.6 |
0 |
0 |
| Arthropoda |
6.33 |
5.71 |
1.4 |
0 |
0 |
| Cnidaria |
3.36 |
0 |
0 |
0 |
0 |
| Echinodermata |
454 |
62.4 |
81.1 |
0 |
0 |
| Mollusca |
44.4 |
77.9 |
31.5 |
0 |
0 |
| Nematoda |
0.0347 |
0.0143 |
0 |
0 |
0 |
| Nemertea |
0.0022 |
1.71 |
0 |
0 |
0 |
| Sipuncula |
0.457 |
0.554 |
0.0842 |
0 |
0 |


## cluster indicator_value probability
## harpacticoida 1 0.3173 0.034
## nematoda 2 0.4158 0.002
## ameritella_agilis 2 0.1612 0.015
## nephtyidae_spp 2 0.1571 0.023
## byblis_gaimardii 2 0.1000 0.036
##
## Sum of probabilities = 67.21
##
## Sum of Indicator Values = 9.67
##
## Sum of Significant Indicator Values = 1.15
##
## Number of Significant Indicators = 5
##
## Significant Indicator Distribution
##
## 1 2
## 1 4
4. Regressions
For the following analyses, independant variables are abiotic parameters and exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.
4.1. Data manipulation
All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
| aquaculture |
1 |
-0.291 |
-0.536 |
-0.572 |
-0.498 |
-0.485 |
0.456 |
| dredging |
-0.291 |
1 |
0.595 |
0.409 |
0.776 |
0.463 |
-0.28 |
| runoff |
-0.536 |
0.595 |
1 |
0.37 |
0.866 |
0.288 |
-0.302 |
| sewers |
-0.572 |
0.409 |
0.37 |
1 |
0.601 |
0.735 |
-0.686 |
| structures |
-0.498 |
0.776 |
0.866 |
0.601 |
1 |
0.469 |
-0.377 |
| shipping |
-0.485 |
0.463 |
0.288 |
0.735 |
0.469 |
1 |
-0.53 |
| fisheries |
0.456 |
-0.28 |
-0.302 |
-0.686 |
-0.377 |
-0.53 |
1 |

4.2. Univariate regressions
We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.
Results of regressions (coefficients with a significant p-value for marginal tests) are shown below. Using both abiotic parameters and exposure indices as predictors do not increase significantly predictive power compared to the other models. Details of the regressions for exposure indices, with diagnostics and cross-validation, are summarized below.
| Depth |
|
|
|
+ |
+ |
|
|
|
|
| Aquaculture |
|
|
|
|
|
|
|
|
|
| Dredging |
|
|
|
|
|
|
|
|
+ |
| Runoff |
|
- |
|
+ |
|
|
|
|
|
| Sewers |
|
- |
- |
|
|
|
|
|
|
| Structures |
|
+ |
|
|
|
|
|
|
|
| Shipping |
|
|
+ |
|
|
|
|
+ |
|
| Fisheries |
|
|
+ |
|
|
|
|
|
|
| Adjusted \(R^{2}\) |
0.02 |
0.04 |
0.16 |
0.29 |
0.14 |
0 |
0.04 |
0.01 |
0.13 |
Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
1.925e-16 |
0.09539 |
2.018e-15 |
1 |
|
| depth |
-0.185 |
0.1122 |
-1.649 |
0.1024 |
|
| aquaculture |
0.05215 |
0.1347 |
0.3871 |
0.6995 |
|
| dredging |
-0.1216 |
0.129 |
-0.9424 |
0.3483 |
|
| runoff |
0.1879 |
0.2201 |
0.854 |
0.3952 |
|
| sewers |
0.1503 |
0.1855 |
0.8101 |
0.4198 |
|
| structures |
-0.1768 |
0.2539 |
-0.6963 |
0.4879 |
|
| shipping |
-0.09248 |
0.133 |
-0.6955 |
0.4884 |
|
| fisheries |
0.122 |
0.1151 |
1.06 |
0.2918 |
|
## RMSE from cross-validation: 1.06337
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Biomass
## Adjusted R2 is: 0.04
Fitting linear model: B ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-6.466e-17 |
0.09425 |
-6.861e-16 |
1 |
|
| depth |
-0.2062 |
0.1109 |
-1.86 |
0.06585 |
|
| aquaculture |
-0.2534 |
0.1331 |
-1.904 |
0.05983 |
|
| dredging |
-0.009733 |
0.1275 |
-0.07637 |
0.9393 |
|
| runoff |
-0.4785 |
0.2174 |
-2.201 |
0.03008 |
* |
| sewers |
-0.5772 |
0.1833 |
-3.149 |
0.002167 |
* * |
| structures |
0.5394 |
0.2509 |
2.15 |
0.03399 |
* |
| shipping |
0.09399 |
0.1314 |
0.7154 |
0.4761 |
|
| fisheries |
-0.09747 |
0.1138 |
-0.8568 |
0.3936 |
|
## RMSE from cross-validation: 1.018014
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Richness
## Adjusted R2 is: 0.16
Fitting linear model: S ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-2.208e-16 |
0.0882 |
-2.504e-15 |
1 |
|
| aquaculture |
0.1387 |
0.1246 |
1.114 |
0.2681 |
|
| dredging |
-0.1659 |
0.1192 |
-1.392 |
0.167 |
|
| runoff |
0.1802 |
0.2009 |
0.8969 |
0.3719 |
|
| sewers |
-0.3168 |
0.1586 |
-1.998 |
0.04846 |
* |
| structures |
-0.05714 |
0.2315 |
-0.2468 |
0.8056 |
|
| shipping |
0.3368 |
0.1164 |
2.894 |
0.004668 |
* * |
| fisheries |
0.2269 |
0.106 |
2.141 |
0.03474 |
* |
## RMSE from cross-validation: 0.945198
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

Diversity
## Adjusted R2 is: 0.29
Fitting linear model: H ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-1.513e-16 |
0.08128 |
-1.861e-15 |
1 |
|
| depth |
0.535 |
0.09561 |
5.596 |
1.956e-07 |
* * * |
| aquaculture |
0.1666 |
0.1148 |
1.451 |
0.1499 |
|
| dredging |
0.005753 |
0.1099 |
0.05234 |
0.9584 |
|
| runoff |
0.4163 |
0.1875 |
2.22 |
0.02868 |
* |
| sewers |
0.002447 |
0.1581 |
0.01548 |
0.9877 |
|
| structures |
-0.3441 |
0.2163 |
-1.59 |
0.115 |
|
| shipping |
0.1112 |
0.1133 |
0.9812 |
0.3289 |
|
| fisheries |
0.02589 |
0.0981 |
0.2639 |
0.7924 |
|
## RMSE from cross-validation: 0.889925
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

Evenness
## Adjusted R2 is: 0.14
Fitting linear model: J ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-5.164e-16 |
0.08921 |
-5.789e-15 |
1 |
|
| depth |
0.4573 |
0.1049 |
4.358 |
3.213e-05 |
* * * |
| aquaculture |
0.06496 |
0.126 |
0.5156 |
0.6073 |
|
| dredging |
0.1359 |
0.1206 |
1.126 |
0.2627 |
|
| runoff |
0.3196 |
0.2058 |
1.553 |
0.1236 |
|
| sewers |
0.06674 |
0.1735 |
0.3846 |
0.7013 |
|
| structures |
-0.3271 |
0.2375 |
-1.377 |
0.1715 |
|
| shipping |
-0.04701 |
0.1244 |
-0.3779 |
0.7063 |
|
| fisheries |
-0.1252 |
0.1077 |
-1.163 |
0.2478 |
|
## RMSE from cross-validation: 0.986413
Variance Inflation Factors
| VIF |
1.17 |
1.41 |
1.35 |
2.3 |
1.94 |
2.65 |
1.39 |
1.2 |

AMBI
## Adjusted R2 is: -0.03
Fitting linear model: AMBI ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
-2.507e-16 |
0.09773 |
-2.565e-15 |
1 |
|
| aquaculture |
-0.06949 |
0.138 |
-0.5035 |
0.6157 |
|
| dredging |
0.06339 |
0.132 |
0.4801 |
0.6322 |
|
| runoff |
-0.1955 |
0.2226 |
-0.8784 |
0.3818 |
|
| sewers |
-0.06005 |
0.1757 |
-0.3417 |
0.7333 |
|
| structures |
0.2331 |
0.2565 |
0.9086 |
0.3658 |
|
| shipping |
-0.1358 |
0.1289 |
-1.053 |
0.2947 |
|
| fisheries |
0.03426 |
0.1174 |
0.2917 |
0.7711 |
|
## RMSE from cross-validation: 1.047711
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

M-AMBI
## Adjusted R2 is: 0.04
Fitting linear model: M_AMBI ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
2.465e-16 |
0.09426 |
2.616e-15 |
1 |
|
| aquaculture |
0.1612 |
0.1331 |
1.211 |
0.2287 |
|
| dredging |
-0.2092 |
0.1274 |
-1.643 |
0.1036 |
|
| runoff |
0.4025 |
0.2147 |
1.875 |
0.06374 |
|
| sewers |
-0.04131 |
0.1695 |
-0.2437 |
0.8079 |
|
| structures |
-0.231 |
0.2474 |
-0.9338 |
0.3526 |
|
| shipping |
0.1189 |
0.1244 |
0.9562 |
0.3413 |
|
| fisheries |
0.09677 |
0.1133 |
0.8543 |
0.395 |
|
## RMSE from cross-validation: 1.009459
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

BENTIX
## Adjusted R2 is: 0.01
Fitting linear model: BENTIX ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
2.237e-16 |
0.09562 |
2.339e-15 |
1 |
|
| aquaculture |
0.1808 |
0.135 |
1.339 |
0.1835 |
|
| dredging |
-0.02571 |
0.1292 |
-0.199 |
0.8427 |
|
| runoff |
0.33 |
0.2178 |
1.515 |
0.1328 |
|
| sewers |
0.1123 |
0.1719 |
0.6532 |
0.5151 |
|
| structures |
-0.2833 |
0.251 |
-1.129 |
0.2616 |
|
| shipping |
0.2649 |
0.1262 |
2.1 |
0.03828 |
* |
| fisheries |
0.02871 |
0.1149 |
0.2499 |
0.8032 |
|
## RMSE from cross-validation: 1.034627
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

BOPA
## Adjusted R2 is: 0.13
Fitting linear model: BOPA ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
| (Intercept) |
6.155e-17 |
0.08975 |
6.858e-16 |
1 |
|
| aquaculture |
0.02663 |
0.1268 |
0.2101 |
0.834 |
|
| dredging |
0.4859 |
0.1213 |
4.007 |
0.0001184 |
* * * |
| runoff |
0.02979 |
0.2044 |
0.1457 |
0.8844 |
|
| sewers |
0.1388 |
0.1614 |
0.86 |
0.3918 |
|
| structures |
-0.2214 |
0.2356 |
-0.9398 |
0.3496 |
|
| shipping |
0.02867 |
0.1184 |
0.2421 |
0.8092 |
|
| fisheries |
0.02777 |
0.1079 |
0.2574 |
0.7974 |
|
## RMSE from cross-validation: 1.056417
Variance Inflation Factors
| VIF |
1.41 |
1.34 |
2.27 |
1.79 |
2.61 |
1.31 |
1.2 |

4.3. Multivariate regressions
Single
Aquaculture
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ aquaculture, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5405 1.8407 0.034 *
## Residual 106 31.1271
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Dredging
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ dredging, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.6326 2.1605 0.012 *
## Residual 106 31.0351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Runoff
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ runoff, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5265 1.7922 0.026 *
## Residual 106 31.1411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Sewers
## Adjusted R2 is: 0.04
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ sewers, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 1.4864 5.2205 0.001 ***
## Residual 106 30.1812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Structures
## Adjusted R2 is: 0.01
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ structures, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.5634 1.9201 0.014 *
## Residual 106 31.1042
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Shipping
## Adjusted R2 is: 0.05
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ shipping, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 1.7855 6.3335 0.001 ***
## Residual 106 29.8822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Fisheries
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ fisheries, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.809 2.7789 0.003 **
## Residual 106 30.859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Cumulative exposure
## Adjusted R2 is: 0.02
## Permutation test for dbrda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: dbrda(formula = sp_L ~ cumulative_exposure, data = var_full_S, distance = "bray")
## Df SumOfSqs F Pr(>F)
## Model 1 0.9292 3.2042 0.001 ***
## Residual 106 30.7385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Multiple
Stations are colored according to the cumulative exposure index (cf. Marine Strategy Framework Directive):
- indigo = lowest exposure (\(E_{ij}\) < 1.4) ~ high status
- green = low exposure (1.4 ≤ \(E_{ij}\) < 2.8) ~ good status
- yellow = moderate exposure (2.8 ≤ \(E_{ij}\) < 4.2) ~ moderate status
- orange = high exposure (4.2 ≤ \(E_{ij}\) < 5.6) ~ poor status
- crimson = highest exposure (\(E_{ij}\) ≥ 5.6) ~ bad status
Using vegan
The model has a \(R^{2}\) of 0.23 for exposure indices and 0.34 for all variables.


Using PRIMER-e
The model evaluated by the DistLM procedure has a \(R^{2}\) of 0.22 for exposure indices and 0.34 for all variables.


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